SomaLogic proteomics reveals new biomarkers and provides mechanistic, clinical insights into Acetyl coA Carboxylase (ACC) inhibition in Non-alcoholic Steatohepatitis (NASH).

Non-alcoholic Fatty Liver Disease (NAFLD) and Non-alcoholic Steatohepatitis (NASH) are major metabolic diseases with increasing global prevalence and no approved therapies. There is a mounting need to develop biomarkers of diagnosis, prognosis and treatment response that can effectively replace current requirements for liver biopsies, which are invasive, error-prone and expensive. We performed SomaLogic serum proteome profiling with baseline (n = 231) and on-treatment (n = 72, Weeks 12 and 16, Placebo and 25 mg PF-05221304) samples from a Phase 2a trial (NCT03248882) with Clesacostat (PF-05221304), an acetyl coA carboxylase inhibitor (ACCi) in patients with NAFLD/NASH. SomaSignal NASH probability scores and expression data for 7000+ analytes were analyzed to identify potential biomarkers associated with baseline clinical measures of NAFLD/NASH [Magnetic Resonance Imaging-Proton Density Fat Fraction (MRI-PDFF), alanine aminotransferase (ALT) and aspartate aminotransferase (AST)] as well as biomarkers of treatment response to ACCi. SomaSignal NASH probability scores identified biopsy-proven/clinically defined NIT-based (Presumed) NASH classification of the cohort with > 70% agreement. Clesacostat-induced reduction in steatosis probability scores aligned with observed clinical reduction in hepatic steatosis based on MRI-PDFF. We identify a set of 69 analytes that robustly correlate with clinical measures of hepatic inflammation and steatosis (MRI-PDFF, ALT and AST), 27 of which were significantly reversed with ACC inhibition. Clesacostat treatment dramatically upregulated Wnt5a protein and Apolipoproteins C3 and E, with drug-induced changes significantly correlating to changes on MRI-PDFF. Our data demonstrate the utility of SomaLogic- analyte panel for diagnosis and treatment response in NAFLD/NASH and provide potential new mechanistic insights into liver steatosis reduction, inflammation and serum triglyceride elevation with ACC inhibition. (Clinical Trial Identifier: NCT03248882).

A Novel GUCY2C-CD3 T-Cell Engaging Bispecific Construct (PF-07062119) for the Treatment of Gastrointestinal Cancers.

PURPOSE: Gastrointestinal cancers remain areas of high unmet need despite advances in targeted and immunotherapies. Here, we demonstrate potent, tumor-selective efficacy with PF-07062119, a T-cell engaging CD3 bispecific targeting tumors expressing Guanylyl Cyclase C (GUCY2C), which is expressed widely across colorectal cancer and other gastrointestinal malignancies. In addition, to address immune evasion mechanisms, we explore combinations with immune checkpoint blockade agents and with antiangiogenesis therapy. EXPERIMENTAL DESIGN: PF-07062119 activity was evaluated in vitro in multiple tumor cell lines, and in vivo in established subcutaneous and orthotopic human colorectal cancer xenograft tumors with adoptive transfer of human T cells. Efficacy was also evaluated in mouse syngeneic tumors using human CD3ε transgenic mice. IHC and mass cytometry were performed to demonstrate drug biodistribution, recruitment of activated T cells, and to identify markers of immune evasion. Combination studies were performed with anti-PD-1/PD-L1 and anti-VEGF antibodies. Toxicity and pharmacokinetic studies were done in cynomolgus macaque. RESULTS: We demonstrate that GUCY2C-positive tumors can be targeted with an anti-GUCY2C/anti-CD3ε bispecific, with selective drug biodistribution to tumors. PF-07062119 showed potent T-cell-mediated in vitro activity and in vivo efficacy in multiple colorectal cancer human xenograft tumor models, including KRAS- and BRAF-mutant tumors, as well as in the immunocompetent mouse syngeneic tumor model. PF-07062119 activity was further enhanced when combined with anti-PD-1/PD-L1 treatment or in combination with antiangiogenic therapy. Toxicity studies in cynomolgus indicated a monitorable and manageable toxicity profile. CONCLUSIONS: These data highlight the potential for PF-07062119 to demonstrate efficacy and improve patient outcomes in colorectal cancer and other gastrointestinal malignancies.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin's lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1).

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate.

The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.